# Green AI
**Wikidata**: [Q103845039](https://www.wikidata.org/wiki/Q103845039)  
**Source**: https://4ort.xyz/entity/green-ai

## Summary
Green AI is a subfield of artificial intelligence focused on reducing the environmental impact of AI systems, particularly their energy consumption and carbon emissions. It aims to develop more efficient algorithms, hardware, and deployment practices to mitigate the ecological footprint of AI technologies.

## Key Facts
- Green AI is a subclass of artificial intelligence.
- It specifically addresses the high energy consumption and associated carbon emissions of AI training and inference.
- The field emphasizes optimizing algorithms for computational efficiency and reducing hardware requirements.
- Green AI research explores techniques like model pruning, quantization, and knowledge distillation to minimize resource use.
- It considers the entire lifecycle of AI systems, from data centers to end-user devices, to assess environmental impact.
- There is no single universally adopted metric for measuring "greenness" in AI systems.
- The concept has gained prominence as AI adoption scales and energy concerns grow.

## FAQs
### Q: What is the primary goal of Green AI?
A: The primary goal of Green AI is to minimize the environmental impact of artificial intelligence by reducing its energy consumption and carbon emissions throughout its lifecycle.

### Q: Why is Green AI becoming important now?
A: Green AI is becoming important due to the rapid growth in AI deployment, leading to significant increases in energy use and associated environmental costs, raising sustainability concerns.

### Q: How does Green AI differ from traditional AI?
A: Green AI differs by explicitly prioritizing energy efficiency and environmental sustainability as core design principles, whereas traditional AI primarily focuses on performance and accuracy.

### Q: What are common techniques used in Green AI?
A: Common techniques include algorithmic optimization (pruning, quantization), hardware efficiency improvements, using smaller models, and optimizing data center operations.

## Why It Matters
Green AI addresses the critical challenge of reconcancing technological advancement with environmental responsibility. As AI models grow larger and more complex, their energy demands strain power grids and contribute significantly to carbon footprints. Developing efficient AI is essential for sustainable technological progress, enabling the benefits of AI without exacerbating climate change or depleting resources. It ensures the long-term viability of AI by making it economically and environmentally feasible at scale, aligning innovation with global sustainability goals.

## Notable For
- Explicitly integrating environmental sustainability as a core objective within AI development.
- Pioneering research into quantifying and reducing the energy consumption of large-scale AI models.
- Promoting the adoption of efficiency-first design principles in AI research and industry practices.
- Highlighting the often-overlooked environmental costs of AI progress and advocating for mitigation strategies.

## Body
### Energy Efficiency Focus
Green AI centers on reducing the computational resources required for AI tasks. This involves optimizing algorithms to perform the same or similar tasks with fewer operations or less data. Techniques like model compression and efficient architectures are central to this approach.

### Lifecycle Consideration
The field examines the environmental impact across the entire AI lifecycle, including data collection and processing, model training, inference (deployment), and hardware manufacturing and disposal. This holistic view ensures sustainability efforts target the most significant energy and emission sources.

### Environmental Impact Awareness
Green AI raises awareness about the substantial carbon footprint associated with training large models, which can be equivalent to the emissions of hundreds of round-trip transatlantic flights. This awareness drives the development of more responsible AI practices.

```json
{
  "@context": "https://schema.org",
  "@type": "Thing",
  "name": "Green AI",
  "description": "A subfield of artificial intelligence focused on reducing the environmental impact of AI systems, particularly energy consumption and carbon emissions.",
  "additionalType": "artificial intelligence"
}

## References

1. [Source](https://vimeo.com/473074499)